Puspasari, Ira, Kusumawati, Weny Indah, Oktarina, Eka Sari, and Jusak, Jusak (2020) A new heart sound signal identification approach suitable for smart healthcare system. In: Proceedings of the 2nd International Conference on Applied Engineering. From: ICAE 2019: 2nd International Conference on Applied Engineering, 2-3 October 2019, Batam, Indonesia.

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Abstract

Currently, proliferation of the Internet of Things development has fostered several research efforts and works in smart healthcare systems. One of the important area is the development of automated systems to identify fundamental heart sound signals in the presence of noise and murmur. In this work, we proposed a new scheme for the fundamental heart sound signals identification and murmur by firstly employing the CEEMD algorithm combined with the Pearson distance metric and secondly utilizing the Shannon energy envelope to segment the first fundamental heart sound signal, S1 the second fundamental heart sound signal, S2, and murmur. Here, the CEEMD algorithm and the Pearson distance metric perform signal decomposition to extract murmur from the heart sound signals while the Shannon energy envelope increases the quality of signal features. Based on our evaluation the proposed algorithm showed promising result to identify the S1 and S2 in the presence of murmur. The algorithm at the same time can trace the duration and location of the murmur.

Item ID:	69258
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-7281-2807-8
Keywords:	heart sound signal, healthcare, identification, empirical mode decomposition, Shannon energy
Copyright Information:	© IEEE
Projects and Grants:	Minister of Research Technology and Higher Education, Republic Indonesia
Date Deposited:	08 Sep 2021 03:09
FoR Codes:	40 ENGINEERING > 4006 Communications engineering > 400607 Signal processing @ 60% 40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified @ 40%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 100%
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